Particles containing black carbon (BC) alter the Earth's energy balance by scattering and absorbing solar radiation, by interacting with clouds, and by decreasing the albedo of ice and snow. Each of these climate effects depends on the properties of individual BC-containing particles and their atmospheric residence time. The dominant removal mechanism of BC mass from the atmosphere is wet deposition, with one important pathway being the activation of BC-containing particles into cloud condensation nuclei (CCN) and their subsequent removal if the cloud precipitates. Although freshly emitted BC-containing particles are too small and hydrophobic to activate, their morphology and chemical composition are altered soon after emission by condensation of semi-volatile gases and coagulation with pre-existing particles. This transformation in black carbon's characteristics, termed ``aging", increases particles' susceptibility to cloud droplet nucleation and wet removal. Further, these aging process also modify light absorption and scattering by particles containing black carbon. However, a complex aerosol population that evolves with time is not easily simulated in climate models, so even sophisticated aerosol schemes do not fully resolve aerosol properties on a per-particle level.
The objective of this research is (1) to improve the scientific understanding of the underlying factors that drive aerosol aging, (2) to quantify error in climate-relevant aerosol properties for the approximate representations of aerosol microphysical properties that are commonly applied in global models, and (3) to produce parameterizations that enable improved representations of black carbon within existing global model frameworks. Particle-resolved model simulations were used to complete these objectives. First, I applied a process-level analysis to identify the set of independent variables that best explain variance in BC's aging timescale for a large collection of simulations. I show that 80-90% of variance in BC's aging timescale is explained by just a few independent variables. Second, I used PartMC-MOSAIC as a benchmark for comparing other aerosol modeling frameworks. I found that cloud condensation nuclei activity can often be modeled with high accuracy using very simple representations of aerosol composition, but more complex representations of aerosol composition are needed to simulate aerosol absorption. Finally, based on particle-resolved simulations I developed one parameterization to represent black carbon's aging timescale and a different parameterization to predict enhancement in light absorption by mixed BC populations.